Feedforward Neural Networks
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Feedforward Neural Networks
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-24
Feedforward Neural Networks written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-24 with Computers categories.
What Is Feedforward Neural Networks A feedforward neural network, often known as a FNN, is a type of artificial neural network that does not have connections that form a cycle between its nodes. Therefore, it is distinct from its offspring, which are known as recurrent neural networks. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Feedforward neural network Chapter 2: Artificial neural network Chapter 3: Perceptron Chapter 4: Artificial neuron Chapter 5: Multilayer perceptron Chapter 6: Delta rule Chapter 7: Backpropagation Chapter 8: Types of artificial neural networks Chapter 9: Learning rule Chapter 10: Mathematics of artificial neural networks (II) Answering the public top questions about feedforward neural networks. (III) Real world examples for the usage of feedforward neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of feedforward neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Feed Forward Neural Networks
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Author : Anne-Johan Annema
language : en
Publisher: Springer Science & Business Media
Release Date : 1995-05-31
Feed Forward Neural Networks written by Anne-Johan Annema and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-05-31 with Science categories.
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.
Feedforward Neural Network Methodology
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Author : Terrence L. Fine
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06
Feedforward Neural Network Methodology written by Terrence L. Fine and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Computers categories.
This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
Nonlinear Dynamical Systems
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Author : Irwin W. Sandberg
language : en
Publisher: John Wiley & Sons
Release Date : 2001-02-21
Nonlinear Dynamical Systems written by Irwin W. Sandberg and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-21 with Technology & Engineering categories.
Sechs erfahrene Autoren beschreiben in diesem Band ein Spezialgebiet der neuronalen Netze mit Anwendungen in der Signalsteuerung, Signalverarbeitung und Zeitreihenanalyse. Ein zeitgemäßer Beitrag zur Behandlung nichtlinear-dynamischer Systeme!
Neural Smithing
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Author : Russell Reed
language : en
Publisher: MIT Press
Release Date : 1999-02-17
Neural Smithing written by Russell Reed and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-02-17 with Computers categories.
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
On The Application Of Feedforward Neural Networks
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Author : Kevan Sayed Hashemi
language : en
Publisher:
Release Date : 1993
On The Application Of Feedforward Neural Networks written by Kevan Sayed Hashemi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.
Sequences Of Near Optimal Feedforward Neural Networks
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Author : Pramod Lakshmi Narasimha
language : en
Publisher: ProQuest
Release Date : 2007
Sequences Of Near Optimal Feedforward Neural Networks written by Pramod Lakshmi Narasimha and has been published by ProQuest this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Electrical engineering categories.
In order to facilitate complexity optimization in feedforward networks, several inte- grated growing and pruning algorithms are developed. First, a growing scheme is reviewed which iteratively adds new hidden units to full-trained networks. Then, a non-heuristic one-pass pruning technique is reviewed, which utilizes orthogonal least squares. Based upon pruning, a one-pass approach is developed for producing the validation error versus network size curve. Then, a combined approach is devised in which grown networks are pruned. As a result, the hidden units are ordered according to their usefulness, and less useful units are eliminated. In several examples, it is shown that networks designed using the integrated growing and pruning method have less training and validation error. This combined method exhibits reduced sensitivity to the choice of the initial weights and produces an almost monotonic error versus network size curve. Starting from the strict interpolation equations for multivariate polynomials, an upper bound is developed for the number of patterns that can be memorized by a non-linear feedforward network. A straightforward proof by contradiction is presented for the upper bound. It is shown that the hidden activations do not have to be analytic. Networks, trained by conjugate gradient, are used to demonstrate the tightness of the bound for random patterns. The theoretical results agree closely to the simulations on two class problems solved by support vector machines. We model large classifiers like Support Vector Machines (SVMs) by smaller networks in order to decrease the computational cost. The key idea is to generate additional training patterns using a trained SVM and use these additional patterns along with the original training patterns to train a much smaller neural network. Results shown verify the validity of the technique and the method used to generate additional patterns. We also generalize this idea and prove that any learning machine can be used to generate additional patterns and in turn train any other machine to improve its performance.
Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data
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Author : Gregory L. Tarr
language : en
Publisher:
Release Date : 1988
Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data written by Gregory L. Tarr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computer vision categories.
An environment is developed for the study of dynamic changes in patterns of weight and node values for artificial neural networks. Graphic representations of neural network internal states are displayed using a high resolution video terminal. Patterns of node firings and changes in weight vectors are displayed to provide insight during training. Four pattern recognition problems are applied to four types of artificial neural networks. Using simulated data, a simple disjoint region classification problem is developed and examined using a Kohonen net and a multilayer feedforward back propagation (MFB) network. A MFB neural network is also used to simulate a Fourier filter. Using a Kohonen net, a MFB, a counterpropagation and a hybrid network, data measured from infrared and laser radar imagery of military vehicles is analyzed. The accuracy and training times for a MFB net and a Hybrid net are compared using an ambiguous decision region problem. Each classification problem is examined and compared to classical, nearest neighbor pattern recognition techniques. Using dynamic analysis, neural network is developed using Kohonen training rules for the first hidden layer followed by one or two hidden layers using standard back propagation rules for training. Advantage of the hybrid network is shown for classification problems involving anomalies characteristic of measured data. The Hybrid network requires less training and fewer interconnections than MFB when classifications involves ambiguous decision regions. Theses. (RH).
Advances In Neural Networks Isnn 2007
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Author : Derong Liu
language : en
Publisher: Springer
Release Date : 2007-07-14
Advances In Neural Networks Isnn 2007 written by Derong Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-14 with Computers categories.
This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.
Multi Layered Feedforward Neural Networks For Image Segmentation
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Author : Gregory Lynn Tarr
language : en
Publisher:
Release Date : 1991
Multi Layered Feedforward Neural Networks For Image Segmentation written by Gregory Lynn Tarr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computer vision categories.